



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest
class 					_snake_case      (       _A):
				UpperCamelCase__					:       Any  		=(CMStochasticIterativeScheduler,)
				UpperCamelCase__					:       int  		=1_0




				def 				A__   (    self       :    Any,       **__lowercase       :    List[str]    ):
							lowercase__       	=					{
							    "num_train_timesteps": 201,
							    "sigma_min": 0.002,
							    "sigma_max": 80.0,
							}

							config.update(**UpperCamelCase__    )
							return config




				def 				A__   (    self       :    List[Any]    ):
							lowercase__       	=					10

							lowercase__       	=					self.get_scheduler_config()
							lowercase__       	=					self.scheduler_classes[0](**UpperCamelCase__    )

							scheduler.set_timesteps(UpperCamelCase__    )

							lowercase__       	=					scheduler.timesteps[0]
							lowercase__       	=					scheduler.timesteps[1]

							lowercase__       	=					self.dummy_sample
							lowercase__       	=					0.1 * sample

							lowercase__       	=					scheduler.step(UpperCamelCase__,       UpperCamelCase__,       UpperCamelCase__    ).prev_sample
							lowercase__       	=					scheduler.step(UpperCamelCase__,       UpperCamelCase__,       UpperCamelCase__    ).prev_sample

							self.assertEqual(output_a.shape,       sample.shape    )
							self.assertEqual(output_a.shape,       output_a.shape    )




				def 				A__   (    self       :    Dict    ):






							for timesteps in [10, 50, 100, 1000]:
										self.check_over_configs(num_train_timesteps=UpperCamelCase__    )




				def 				A__   (    self       :    List[Any]    ):






							for clip_denoised in [True, False]:
										self.check_over_configs(clip_denoised=UpperCamelCase__    )




				def 				A__   (    self       :    Union[str, Any]    ):
							lowercase__       	=					self.scheduler_classes[0]
							lowercase__       	=					self.get_scheduler_config()
							lowercase__       	=					scheduler_class(**UpperCamelCase__    )

							lowercase__       	=					1
							scheduler.set_timesteps(UpperCamelCase__    )
							lowercase__       	=					scheduler.timesteps

							lowercase__       	=					torch.manual_seed(0    )

							lowercase__       	=					self.dummy_model()
							lowercase__       	=					self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(UpperCamelCase__    ):
										# 1. scale model input
										lowercase__       	=					scheduler.scale_model_input(UpperCamelCase__,       UpperCamelCase__    )

										# 2. predict noise residual
										lowercase__       	=					model(UpperCamelCase__,       UpperCamelCase__    )

										# 3. predict previous sample x_t-1
										lowercase__       	=					scheduler.step(UpperCamelCase__,       UpperCamelCase__,       UpperCamelCase__,       generator=UpperCamelCase__    ).prev_sample

										lowercase__       	=					pred_prev_sample

							lowercase__       	=					torch.sum(torch.abs(UpperCamelCase__    )    )
							lowercase__       	=					torch.mean(torch.abs(UpperCamelCase__    )    )

							assert abs(result_sum.item() - 192.7614    ) < 1e-2
							assert abs(result_mean.item() - 0.2510    ) < 1e-3




				def 				A__   (    self       :    Union[str, Any]    ):
							lowercase__       	=					self.scheduler_classes[0]
							lowercase__       	=					self.get_scheduler_config()
							lowercase__       	=					scheduler_class(**UpperCamelCase__    )

							lowercase__       	=					[106, 0]
							scheduler.set_timesteps(timesteps=UpperCamelCase__    )
							lowercase__       	=					scheduler.timesteps

							lowercase__       	=					torch.manual_seed(0    )

							lowercase__       	=					self.dummy_model()
							lowercase__       	=					self.dummy_sample_deter * scheduler.init_noise_sigma

							for t in timesteps:
										# 1. scale model input
										lowercase__       	=					scheduler.scale_model_input(UpperCamelCase__,       UpperCamelCase__    )

										# 2. predict noise residual
										lowercase__       	=					model(UpperCamelCase__,       UpperCamelCase__    )

										# 3. predict previous sample x_t-1
										lowercase__       	=					scheduler.step(UpperCamelCase__,       UpperCamelCase__,       UpperCamelCase__,       generator=UpperCamelCase__    ).prev_sample

										lowercase__       	=					pred_prev_sample

							lowercase__       	=					torch.sum(torch.abs(UpperCamelCase__    )    )
							lowercase__       	=					torch.mean(torch.abs(UpperCamelCase__    )    )

							assert abs(result_sum.item() - 347.6357    ) < 1e-2
							assert abs(result_mean.item() - 0.4527    ) < 1e-3




				def 				A__   (    self       :    Tuple    ):
							lowercase__       	=					self.scheduler_classes[0]
							lowercase__       	=					self.get_scheduler_config()
							lowercase__       	=					scheduler_class(**UpperCamelCase__    )

							lowercase__       	=					[39, 30, 12, 15, 0]

							with self.assertRaises(UpperCamelCase__,       msg="`timesteps` must be in descending order."    ):
										scheduler.set_timesteps(timesteps=UpperCamelCase__    )




				def 				A__   (    self       :    Optional[int]    ):
							lowercase__       	=					self.scheduler_classes[0]
							lowercase__       	=					self.get_scheduler_config()
							lowercase__       	=					scheduler_class(**UpperCamelCase__    )

							lowercase__       	=					[39, 30, 12, 1, 0]
							lowercase__       	=					len(UpperCamelCase__    )

							with self.assertRaises(UpperCamelCase__,       msg="Can only pass one of `num_inference_steps` or `timesteps`."    ):
										scheduler.set_timesteps(num_inference_steps=UpperCamelCase__,       timesteps=UpperCamelCase__    )




				def 				A__   (    self       :    List[Any]    ):
							lowercase__       	=					self.scheduler_classes[0]
							lowercase__       	=					self.get_scheduler_config()
							lowercase__       	=					scheduler_class(**UpperCamelCase__    )

							lowercase__       	=					[scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    UpperCamelCase__,       msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}",       ):
										scheduler.set_timesteps(timesteps=UpperCamelCase__    )






import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
    import bsa
    from bsa import BeautifulSoup


__lowerCAmelCase     :	int       			=  logging.get_logger(__name__)




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					Union[str, Any]     ,				**UpperCamelCase__     :					Union[str, Any]       )     ->      Union[str, Any]:







    """simple docstring"""






    requires_backends(self     ,				["""bs4"""]       )
    super().__init__(**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]
    __magic_name__								=			element if element.name else element.parent
    for parent in child.parents:  # type: bs4.element.Tag
      __magic_name__								=			parent.find_all(child.name     ,				recursive=UpperCamelCase__       )
      xpath_tags.append(child.name       )
      xpath_subscripts.append(
          0 if 1 == len(UpperCamelCase__       ) else next(i for i, s in enumerate(UpperCamelCase__     ,				1       ) if s is child       )       )
      __magic_name__								=			parent
    xpath_tags.reverse()
    xpath_subscripts.reverse()
    return xpath_tags, xpath_subscripts
  def 				_lowercase				(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			BeautifulSoup(UpperCamelCase__     ,				"""html.parser"""       )

    __magic_name__								=			[]
    __magic_name__								=			[]
    __magic_name__								=			[]

    for element in html_code.descendants:
      if type(UpperCamelCase__       ) == bsa.element.NavigableString:
        if type(element.parent       ) != bsa.element.Tag:
          continue

        __magic_name__								=			html.unescape(UpperCamelCase__       ).strip()
        if not text_in_this_tag:
          continue

        all_doc_strings.append(UpperCamelCase__       )

        __magic_name__	,       __magic_name__								=			self.xpath_soup(UpperCamelCase__       )
        stringaxtag_seq.append(UpperCamelCase__       )
        stringaxsubs_seq.append(UpperCamelCase__       )

    if len(UpperCamelCase__       ) != len(UpperCamelCase__       ):
      raise ValueError("""Number of doc strings and xtags does not correspond"""       )
    if len(UpperCamelCase__       ) != len(UpperCamelCase__       ):
      raise ValueError("""Number of doc strings and xsubs does not correspond"""       )

    return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			""""""
    for tagname, subs in zip(UpperCamelCase__     ,				UpperCamelCase__       ):
      xpath += F'''/{tagname}'''
      if subs != 0:
        xpath += F'''[{subs}]'''
    return xpath





  def __call__(					self     :					str     ,				UpperCamelCase__     :					Tuple       )     ->      BatchFeature:







    """simple docstring"""






    __magic_name__								=			False

    # Check that strings has a valid type
    if isinstance(UpperCamelCase__     ,				UpperCamelCase__       ):
      __magic_name__								=			True
    elif isinstance(UpperCamelCase__     ,				(list, tuple)       ):
      if len(UpperCamelCase__       ) == 0 or isinstance(html_strings[0]     ,				UpperCamelCase__       ):
        __magic_name__								=			True

    if not valid_strings:
      raise ValueError(
          """HTML strings must of type `str`, `List[str]` (batch of examples), """
          F'''but is of type {type(UpperCamelCase__       )}.'''       )

    __magic_name__								=			bool(isinstance(UpperCamelCase__     ,				(list, tuple)       ) and (isinstance(html_strings[0]     ,				UpperCamelCase__       ))       )

    if not is_batched:
      __magic_name__								=			[html_strings]

    # Get nodes + xpaths
    __magic_name__								=			[]
    __magic_name__								=			[]
    for html_string in html_strings:
      __magic_name__	,       __magic_name__	,       __magic_name__								=			self.get_three_from_single(UpperCamelCase__       )
      nodes.append(UpperCamelCase__       )
      __magic_name__								=			[]
      for node, tag_list, sub_list in zip(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       ):
        __magic_name__								=			self.construct_xpath(UpperCamelCase__     ,				UpperCamelCase__       )
        xpath_strings.append(UpperCamelCase__       )
      xpaths.append(UpperCamelCase__       )

    # return as Dict
    __magic_name__								=			{"""nodes""": nodes, """xpaths""": xpaths}
    __magic_name__								=			BatchFeature(data=UpperCamelCase__     ,				tensor_type=UpperCamelCase__       )

    return encoded_inputs

